{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COCO Reader with augmentations\n",
    "\n",
    "Reader operator that reads a COCO dataset (or subset of COCO), which consists of an annotation file and the images directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from nvidia.dali.pipeline import Pipeline\n",
    "import nvidia.dali.ops as ops\n",
    "import nvidia.dali.types as types\n",
    "import numpy as np\n",
    "from time import time\n",
    "import os.path\n",
    "\n",
    "test_data_root = os.environ['DALI_EXTRA_PATH']\n",
    "file_root = os.path.join(test_data_root, 'db', 'coco', 'images')\n",
    "\n",
    "annotations_file = os.path.join(test_data_root, 'db', 'coco', 'instances.json')\n",
    "num_gpus = 1\n",
    "batch_size = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create reader, decoder and flip operator for images and bounding boxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class COCOPipeline(Pipeline):\n",
    "    def __init__(self, batch_size, num_threads, device_id):\n",
    "        super(COCOPipeline, self).__init__(\n",
    "            batch_size, num_threads, device_id, seed=15)\n",
    "        self.input = ops.COCOReader(\n",
    "            file_root=file_root,\n",
    "            annotations_file=annotations_file,\n",
    "            shard_id=device_id,\n",
    "            num_shards=num_gpus,\n",
    "            ratio=True,\n",
    "            ltrb=True)\n",
    "        self.decode = ops.ImageDecoder(device=\"mixed\", output_type=types.RGB)\n",
    "        self.flip = ops.Flip(device=\"gpu\")\n",
    "        self.bbflip = ops.BbFlip(device=\"cpu\", ltrb=True)\n",
    "        self.paste_pos = ops.Uniform(range=(0, 1))\n",
    "        self.paste_ratio = ops.Uniform(range=(1, 2))\n",
    "        self.coin = ops.CoinFlip(probability=0.5)\n",
    "        self.coin2 = ops.CoinFlip(probability=0.5)\n",
    "        self.paste = ops.Paste(device=\"gpu\", fill_value=(32, 64, 128))\n",
    "        self.bbpaste = ops.BBoxPaste(device=\"cpu\", ltrb=True)\n",
    "        self.prospective_crop = ops.RandomBBoxCrop(\n",
    "            device=\"cpu\",\n",
    "            aspect_ratio=[0.5, 2.0],\n",
    "            thresholds=[0.1, 0.3, 0.5],\n",
    "            scaling=[0.8, 1.0],\n",
    "            ltrb=True)\n",
    "        self.slice = ops.Slice(device=\"gpu\")\n",
    "\n",
    "    def define_graph(self):\n",
    "        rng = self.coin()\n",
    "        rng2 = self.coin2()\n",
    "\n",
    "        inputs, bboxes, labels = self.input()\n",
    "        images = self.decode(inputs)\n",
    "\n",
    "        # Paste and BBoxPaste need to use same scales and positions\n",
    "        ratio = self.paste_ratio()\n",
    "        px = self.paste_pos()\n",
    "        py = self.paste_pos()\n",
    "        images = self.paste(images, paste_x=px, paste_y=py, ratio=ratio)\n",
    "        bboxes = self.bbpaste(bboxes, paste_x=px, paste_y=py, ratio=ratio)\n",
    "\n",
    "        crop_begin, crop_size, bboxes, labels = self.prospective_crop(bboxes, labels)\n",
    "        images = self.slice(images, crop_begin, crop_size)\n",
    "\n",
    "        images = self.flip(images, horizontal=rng, vertical=rng2)\n",
    "        bboxes = self.bbflip(bboxes, horizontal=rng, vertical=rng2)\n",
    "\n",
    "        return (images, bboxes, labels)                                           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computation graph built and dataset loaded in 0.331061 seconds.\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "pipes = [COCOPipeline(batch_size=batch_size, num_threads=2, device_id=device_id)  for device_id in range(num_gpus)]\n",
    "for pipe in pipes:\n",
    "    pipe.build()\n",
    "total_time = time() - start\n",
    "print(\"Computation graph built and dataset loaded in %f seconds.\" % total_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_out = [pipe.run() for pipe in pipes]\n",
    "\n",
    "images_cpu = pipe_out[0][0].as_cpu()\n",
    "bboxes_cpu = pipe_out[0][1]\n",
    "labels_cpu = pipe_out[0][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bounding boxes returned by the operator are lists of floats in format **\\[left, top, right, bottom]** in relative cooradinates (ratio=**True**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18785974, 0.16548043, 0.5594866 , 0.5431505 ]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_index = 4\n",
    "\n",
    "bboxes = bboxes_cpu.at(4)\n",
    "bboxes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the ground truth bounding boxes drawn on the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import random\n",
    "\n",
    "img = images_cpu.at(img_index)\n",
    "\n",
    "H = img.shape[0]\n",
    "W = img.shape[1]\n",
    "fig,ax = plt.subplots(1)\n",
    "\n",
    "ax.imshow(img)\n",
    "bboxes = bboxes_cpu.at(img_index)\n",
    "labels = labels_cpu.at(img_index)\n",
    "categories_set = set()\n",
    "for label in labels:\n",
    "    categories_set.add(label[0])\n",
    "\n",
    "category_id_to_color = dict(\n",
    "    [(cat_id, [random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)]) for cat_id in categories_set])\n",
    "\n",
    "for bbox, label in zip(bboxes, labels):\n",
    "    rect = patches.Rectangle(\n",
    "        (bbox[0] * W, bbox[1] * H), # Absolute corner coordinates\n",
    "        (bbox[2] - bbox[0]) * W,    # Absolute bounding box width\n",
    "        (bbox[3] - bbox[1]) * H,    # Absolute bounding box height\n",
    "        linewidth=1,\n",
    "        edgecolor=category_id_to_color[label[0]],\n",
    "        facecolor='none')\n",
    "    ax.add_patch(rect)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
